Hest-1k: A dataset for spatial transcriptomics and histology image analysis
Spatial transcriptomics enables interrogating the molecular composition of tissue with ever-
increasing resolution and sensitivity. However, costs, rapidly evolving technology, and lack …
increasing resolution and sensitivity. However, costs, rapidly evolving technology, and lack …
Non-generative artificial intelligence (AI) in medicine: advancements and applications in supervised and unsupervised machine learning
Abstract The use of Artificial Intelligence (AI) within pathology and healthcare has advanced
extensively. We have accordingly witnessed increased adoption of various AI tools which …
extensively. We have accordingly witnessed increased adoption of various AI tools which …
Virchow2: Scaling self-supervised mixed magnification models in pathology
Foundation models are rapidly being developed for computational pathology applications.
However, it remains an open question which factors are most important for downstream …
However, it remains an open question which factors are most important for downstream …
[HTML][HTML] Generative ai in medicine and healthcare: Moving beyond the 'peak of inflated expectations'
The rapid development of specific-purpose Large Language Models (LLMs), such as Med-
PaLM, MEDITRON-70B, and Med-Gemini, has significantly impacted healthcare, offering …
PaLM, MEDITRON-70B, and Med-Gemini, has significantly impacted healthcare, offering …
A vision–language foundation model for precision oncology
Clinical decision-making is driven by multimodal data, including clinical notes and
pathological characteristics. Artificial intelligence approaches that can effectively integrate …
pathological characteristics. Artificial intelligence approaches that can effectively integrate …
Exploring scalable medical image encoders beyond text supervision
Abstract Language-supervised pretraining has proven to be a valuable method for extracting
semantically meaningful features from images, serving as a foundational element in …
semantically meaningful features from images, serving as a foundational element in …
Benchmarking foundation models as feature extractors for weakly-supervised computational pathology
Advancements in artificial intelligence have driven the development of numerous pathology
foundation models capable of extracting clinically relevant information. However, there is …
foundation models capable of extracting clinically relevant information. However, there is …
Multimodal whole slide foundation model for pathology
The field of computational pathology has been transformed with recent advances in
foundation models that encode histopathology region-of-interests (ROIs) into versatile and …
foundation models that encode histopathology region-of-interests (ROIs) into versatile and …
When multiple instance learning meets foundation models: advancing histological whole slide image analysis
H Xu, M Wang, D Shi, H Qin, Y Zhang, Z Liu… - Medical Image …, 2025 - Elsevier
Deep multiple instance learning (MIL) pipelines are the mainstream weakly supervised
learning methodologies for whole slide image (WSI) classification. However, it remains …
learning methodologies for whole slide image (WSI) classification. However, it remains …
A comprehensive evaluation of histopathology foundation models for ovarian cancer subtype classification
Histopathology foundation models show great promise across many tasks, but analyses
have been limited by arbitrary hyperparameters. We report the most rigorous single-task …
have been limited by arbitrary hyperparameters. We report the most rigorous single-task …